Blinde Akustische Strahlformung fuer Anwendungen im KFZ - Krueger, Alexander; Warsitz, Ernst; Haeb-Umbach, Reinhold (34. Deutsche Jahrestagung fuer Akustik (DAGA 2008))
In diesem Beitrag werden zwei neuartige akustische Strahlformungsalgorithmen fuer einen Einsatz im KFZ diskutiert. In beiden Verfahren ist fuer jede Frequenzkomponente der Eigenvektor zum groessten Eigenwert eines verallgemeinerten Eigenwertproblems zu bestimmen: bei der einen Varianate stellen die Filterkoeffizienten gerade obigen Eigenvektor dar. Sprachverzerrungen werden mit einem einkanaligen Nachfilter kompensiert. In der anderen Variante, welche die Struktur eines ''Generalized Sidelobe Cancellers'' (GSC) hat, basiert die Blockiermatrix auf obiger Eigenvektorzerlegung.

Blind Speech Separation in Presence of Correlated Noise with Generalized Eigenvector Beamforming - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
This paper considers the convolutive blind source separation of speech sources in the presence of spatially correlated noise. We introduce a method for estimating the scaled mixing matrix from the sources to the microphones even if coherent noise is present. This is achieved by combining time-frequency sparseness with the generalized eigenvalue decomposition of the power spectral density matrix (PSD) of the noisy speech and noise-only microphone signals. Separation is performed by spatial filtering with coefficients constructed by Gram-Schmidt orthogonalization which places spatial nulls at the interferer{^A}?`s direction. Experimental results show that our approach is capable of separating 2 sources in a reverberant environment (RT60=0ms..500ms) degraded by significant directional noise.

Generalized Eigenvector Blind Speech Separation Under Coherent Noise In A GSC Configuration - Tran Vu, Dang Hai; Krueger, Alexander; Haeb-Umbach, Reinhold (International Workshop on Acoustic Echo and Noise Control (IWAENC 2008))
This Paper deals with a new Technique for multi-channel separation of speech signals from convolutive mixtures under coherent noise. We demonstrate how the scaled transfer functions from the sources to the microphones can be estimated even in the presence of stationary coherent noise. The key to this are generalized eigenvalue decompositions of the power spectral density (PSD) matrices of the noisy speech and noise-only microphone signals with a controlled estimation of these matrices exploiting time-frequency sparseness of the speech sources. Separation is further improved by subsequent Gram-Schmidt orthogonalization which places spatial nulls at the interferers{\rq} directions, while noise reduction is improved by employing a novel blocking matrix and adaptive interference canceller in a Generalized Sidelobe Canceller (GSC)-like structure. We report promising experimental results for 2 speech sources with significant coherent noise in reverberant environments (RT60=0oms..500ms).

Speech enhancement with a new generalized eigenvector blocking matrix for application in a generalized sidelobe canceller - Warsitz, Ernst; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008))
The generalized sidelobe canceller by Griffith and Jim is a robust beamforming method to enhance a desired (speech) signal in the presence of stationary noise. Its performance depends to a high degree on the construction of the blocking matrix which produces noise reference signals for the subsequent adaptive interference canceller. Especially in reverberated environments the beamformer may suffer from signal leakage and reduced noise suppression. In this paper a new blocking matrix is proposed. It is based on a generalized eigenvalue problem whose solution provides an indirect estimation of the transfer functions from the source to the sensors. The quality of the new generalized eigenvector blocking matrix is studied in simulated rooms with different reverberation times and is compared to alternatives proposed in the literature.
Keywords: adaptive interference canceller; adaptive signal processing; array signal processing; beamforming method; eigenvalues and eigenfunctions; generalized eigenvector blocking matrix; generalized sidelobe canceller; interference suppression; matrix algebra; noise suppression; speech enhancement; transfer function estimation; transfer functions

Modeling the dynamics of speech and noise for speech feature enhancement in ASR - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008))
In this paper a switching linear dynamical model (SLDM) approach for speech feature enhancement is improved by employing more accurate models for the dynamics of speech and noise. The model of the clean speech feature trajectory is improved by augmenting the state vector to capture information derived from the delta features. Further a hidden noise state variable is introduced to obtain a more elaborated model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried out by a bank of extended Kalman filters, whose outputs are combined according to the a posteriori probability of the individual state models. Experimental results on the AURORA2 database show improved recognition accuracy.
Keywords: a posteriori probability; AURORA2 database; Bayesian inference; Bayes methods; channel bank filters; extended Kalman filter banks; hidden noise state variable; Kalman filters; noise dynamics; speech enhancement; speech feature enhancement; speech feature trajectory; switching linear dynamical model approach

A novel approach to noise estimation in model-based speech feature enhancement - Windmann, Stefan; Haeb-Umbach, Reinhold (2008 ITG Conference on Voice Communication (SprachKommunikation))
In this paper, the noise estimation for model-based speech feature enhancement in automatic speech recognition (ASR) is investigated. Beside a stationary noise prior, three linear state space models for the (cepstral) noise process are considered. We have derived novel EM algorithms for the estimation of the noise model parameters: A blockwise EM algorithm is applied on noise-only input data. It is supposed to be used during the offline training mode of the recognizer. Further a sequential online EM algorithm is employed to adapt the observation variance in recognition mode which works as well under the asumption of a stationary noise prior and a linear state model for the noise. Experiments on the AURORA4 database lead to improved recognition results with the new state model compared to the assumption of stationary noise.

A segmental HMM based on a modified emission probability - Windmann, Stefan; Haeb-Umbach, Reinhold; Leutnant, Volker (2008 ITG Conference on Voice Communication (SprachKommunikation))
In this paper, a novel segmental Hidden Markov Model (HMM) is proposed. The model is based on a modified emission density where additional statistical dependencies between subsequent frames of the speech signal are considered. In the following we derive an effective search strategy for the modified statistical model. Further an approach to parameter reduction is introduced. Experiments were carried out on the AURORA2 database where consistent im

Virtual Cord Protocol (VCP): A flexible DHT-like routing service for sensor networks - Awad, Abdalkarim; Sommer, Christoph; German, Reinhard; Dressler, Falko (2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems)

Requirements and objectives for secure Traffic Information Systems - Dressler, Falko; Sommer, Christoph; Gansen, Tobias; Wischhof, Lars (2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems)

Realistic Simulation of Network Protocols in VANET Scenarios - Sommer, Christoph; Dietrich, Isabel; Dressler, Falko (2007 Mobile Networking for Vehicular Environments)